DocumentCode :
798543
Title :
On the efficient allocation of resources for hypothesis evaluation: a statistical approach
Author :
Chien, Steve ; Gratch, Jonathan ; Burl, Michael
Author_Institution :
Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA
Volume :
17
Issue :
7
fYear :
1995
fDate :
7/1/1995 12:00:00 AM
Firstpage :
652
Lastpage :
665
Abstract :
This paper considers the decision-making problem of selecting a strategy from a set of alternatives on the basis of incomplete information (e.g. a finite number of observations). At any time the system can adopt a particular strategy or decide to gather additional information at some cost. Balancing the expected utility of the new information against the cost of acquiring the information is the central problem that the authors address. In the authors´ approach, the cost and utility of applying a particular strategy to a given problem are represented as random variables from a parametric distribution. By observing the performance of each strategy on a randomly selected sample of problems, one can use parameter estimation techniques to infer statistical models of performance on the general population of problems. These models can then be used to estimate: (1) the utility and cost of acquiring additional information and (2) the desirability of selecting a particular strategy from a set of choices. Empirical results are presented that demonstrate the effectiveness of the hypothesis evaluation techniques for tuning system parameters in a NASA antenna scheduling application
Keywords :
decision theory; learning (artificial intelligence); parameter estimation; resource allocation; statistical analysis; NASA antenna scheduling; decision-making problem; expected utility; hypothesis evaluation; parameter estimation techniques; parametric distribution; random variables; statistical approach; statistical performance models; tuning; Adaptive scheduling; Costs; Decision making; Information analysis; Laboratories; Machine learning; NASA; Problem-solving; Propulsion; Resource management;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.391408
Filename :
391408
Link To Document :
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